Embodiments of the inventive concept relate generally to biometric sensing technologies, and more particularly, to biometric sensors configured for single user authentication.
A biometric sensor is a device that senses one or more characteristics of a biological subject, such as a human, and converts those characteristics into biometric information that can be readily measured or analyzed. Common examples of biometric sensors include devices for sensing voice patterns, facial features, fingerprints, retinal patterns, and bone density, to name just a few.
Biometric sensors typically comprise one or more transducer elements for converting biological signals into electrical or mechanical signals. For instance, some fingerprint sensors use capacitative elements to convert electrical charges of the skin into an electronic fingerprint image. Similarly, some biometric sensors use imaging elements to convert reflected or emitted light into an electronic image of a face, retina, or fingerprint.
Some biometric sensors are “active” in the sense that they project signals onto the biological subjects to identify a response or other feature, and some biometric sensors are “passive” in the sense that they perform sensing without projecting signals onto the biological subjects. Examples of “active” sensors include those that shine light on subjects, such as backlight or a targeted laser light, and those that project certain types of vibrations, such as ultrasound waves.
Although various types of biometric sensors have long been used to gather biological data in scientific, medical, and forensic applications, biometric sensors have only recently begun to achieve widespread adoption in security applications. At present, biometric sensors are increasingly used to authenticate users in a variety of security applications including computer security, automotive security, building and home security, and others.
In a typical biometric authentication system, a biometric sensor captures biometric information from a subject. The captured information is then compared with a plurality of stored templates to determine whether the subject should be authenticated. The templates typically comprise biometric information previously collected from a group of authorized subjects. For instance, in an image-based authentication system, the stored templates typically comprise images of authorized subjects' faces or fingerprints, or encoded information such as Fourier or cosine transform coefficients related to the images. Authentication is performed by comparing the captured biometric information with the templates and authenticating the subject only upon detecting a match between the captured biometric information and one or more of the templates.
The performance of a biometric authentication system is typically measured by the frequency with which it authenticates subjects that should be rejected—referred to as the false positive rate—and the frequency with which it rejects subjects that should be authenticated—referred to as the false negative rate. Conventional biometric authentication systems can achieve false positive and false negative rates of about 1:100,000, meaning that the captured biometric information generates a spurious hit or miss about once in every 100,000 comparisons with the stored templates. Accordingly, where the stored templates comprise several thousand or even million images from various subjects, there is a good chance that false positives and false negatives will occur.
False positives and false negatives occur for a variety of reasons in conventional biometric authentication systems. False negatives can occur, for instance, where too many traits are used to match a subject against a template. As an example, where a captured fingerprint image is required to match every nuance of a stored fingerprint image, there is a good chance that even a correct fingerprint will be erroneously rejected due to minor variations in fingerprinting conditions, such as dirt, moisture, finger positioning, or even minor changes in the subject's fingerprint over time. False positives, on the other hand, can occur where too few traits are used to match a subject against a template. As an example, where only one or two traits of a fingerprint image are used for authentication, there is a significant possibility that the wrong subjects will match those one or two traits by random chance.
Another source of false positives and false negatives lies in differences between the biometric sensors used to authenticate subjects, and the biometric sensors used to record the template information. Such differences can arise, for instance, from variances in sensor manufacturing processes. These differences can cause the sensors to produce different measurements of minute traits, even traits from the same subject. As a result, they can lead to inaccurate authentication.